In [1]:
import pandas as pd
import numpy as np
import holoviews as hv
hv.extension("bokeh")
In [2]:
df_quspin = pd.read_csv("./compute_nb/quspin6.csv")
df_bdg = pd.read_csv("./compute_nb/bdg_eigenvals_full_minvals6.csv")
df_analytical = pd.read_csv("./experiments/ferromagnetic/Analytic.csv")

df_quspin["Source"] = "quspin"
df_bdg["Source"] = "bdg"
df_analytical["Source"] = "analytical"
In [3]:
#Rename and Drop
df_analytical["E_N"] = df_analytical["energy"]
df_analytical["M^2"] = df_analytical["min_cos"]
df_quspin = df_quspin.drop(columns=["identity"])
df_bdg = df_bdg.drop(columns=["binary", "J_term"])
df_analytical = df_analytical.drop(columns = ["energy", "min_cos", "absmin_cos"])
In [ ]:
 
In [4]:
#Not varying h

df_all = pd.concat([df_quspin, df_bdg, df_analytical], sort=False)
df_all = df_all[df_all["h"] == 0]
data = hv.Dataset(df_all)

phase_diagram = data.to(hv.HeatMap,['J','Delta'],'E_N',groupby=["Source"])
phase_diagram.opts(hv.opts.HeatMap(tools=['hover'],colorbar=True, clabel="E/N", width=600, height=600,toolbar='above',title='Energy per Particle, N=6',xrotation=90))
hv.save(phase_diagram, 'energy_checks.html')
phase_diagram
Out[4]:
In [5]:
df_all = pd.concat([df_quspin, df_bdg, df_analytical], sort=False)
df_all = df_all[df_all["Source"] != "bdg"]
data = hv.Dataset(df_all)

phase_diagram = data.to(hv.HeatMap,['J','Delta'],'M^2',groupby=["h", "Source"])
phase_diagram.opts(hv.opts.HeatMap(tools=['hover'],colorbar=True, clabel="M^2", width=600, height=600,toolbar='above',title='QPD N=6, Quspin',xrotation=90))
hv.save(phase_diagram, 'phase_diagram.html')
phase_diagram
Out[5]:
In [6]:
# Computing the difference

df_energy_compare = pd.DataFrame()

df_energy_compare["Delta"] = df_quspin["Delta"]
df_energy_compare["J"] = df_quspin["J"]
df_energy_compare["energy_quspin"] = df_quspin["E_N"]
df_energy_compare["energy_analytics"] = df_analytical["E_N"]
df_energy_compare["energy_bdg"] = df_bdg["E_N"]
df_energy_compare["quspin-bdg"] = abs(df_quspin["E_N"] - df_bdg["E_N"])
df_energy_compare["bdg-analytical"] = abs(df_bdg["E_N"] - df_analytical["E_N"])
df_energy_compare["analytical-quspin"] = abs(df_analytical["E_N"] - df_quspin["E_N"])

data2 = hv.Dataset(df_energy_compare)
In [7]:
phase_diagram = data2.to(hv.HeatMap,['J','Delta'],'quspin-bdg',groupby=[])
phase_diagram.opts(hv.opts.HeatMap(tools=['hover'],colorbar=True, width=600, height=600,toolbar='above',title='Differences',xrotation=90))
hv.save(phase_diagram, 'energy_differences.html')
phase_diagram
Out[7]:
In [124]:
df_energy_compare
Out[124]:
Delta J energy_quspin energy_analytics energy_bdg quspin-bdg bdg-analytical
0 0.000000 0.000000 -0.666667 -0.636620 -0.666667 3.004689e-02 3.004689e-02
1 0.040816 0.040816 -0.667499 -0.638787 -0.667499 2.871144e-02 2.871144e-02
2 0.081633 0.081633 -0.669982 -0.643829 -0.669982 2.615274e-02 2.615274e-02
3 0.122449 0.122449 -0.674081 -0.650938 -0.674081 2.314339e-02 2.314339e-02
4 0.163265 0.163265 -0.679738 -0.659700 -0.679738 2.003859e-02 2.003859e-02
5 0.204082 0.204082 -0.686879 -0.669840 -0.686879 1.703805e-02 1.703805e-02
6 0.244898 0.244898 -0.695415 -0.681160 -0.695415 1.425500e-02 1.425500e-02
7 0.285714 0.285714 -0.705250 -0.693502 -0.705250 1.174845e-02 1.174845e-02
8 0.326531 0.326531 -0.716285 -0.706743 -0.716285 9.542129e-03 9.542129e-03
9 0.367347 0.367347 -0.728419 -0.720782 -0.728419 7.636896e-03 7.636896e-03
10 0.408163 0.408163 -0.741553 -0.735534 -0.741553 6.019201e-03 6.019201e-03
11 0.448980 0.448980 -0.755595 -0.750928 -0.755595 4.667009e-03 4.667009e-03
12 0.489796 0.489796 -0.770458 -0.766904 -0.770458 3.553884e-03 3.553884e-03
13 0.530612 0.530612 -0.786059 -0.783408 -0.786059 2.651743e-03 2.651743e-03
14 0.571429 0.571429 -0.802327 -0.800394 -0.802327 1.932665e-03 1.932665e-03
15 0.612245 0.612245 -0.819192 -0.817822 -0.819192 1.370007e-03 1.370007e-03
16 0.653061 0.653061 -0.836597 -0.835658 -0.836597 9.390428e-04 9.390428e-04
17 0.693878 0.693878 -0.854486 -0.853868 -0.854486 6.172813e-04 6.172813e-04
18 0.734694 0.734694 -0.872811 -0.872426 -0.872811 3.845639e-04 3.845639e-04
19 0.775510 0.775510 -0.891530 -0.891307 -0.891530 2.230257e-04 2.230257e-04
20 0.816327 0.816327 -0.910603 -0.910486 -0.910603 1.169746e-04 1.169746e-04
21 0.857143 0.857143 -0.929998 -0.929946 -0.929998 5.272612e-05 5.272612e-05
22 0.897959 0.897959 -0.949684 -0.949665 -0.949684 1.841676e-05 1.841676e-05
23 0.938776 0.938776 -0.969633 -0.969629 -0.969633 3.814702e-06 3.814702e-06
24 0.979592 0.979592 -0.989822 -0.989822 -0.989822 1.355582e-07 1.355582e-07
25 1.020408 1.020408 -1.010230 -1.010230 -1.010230 1.301367e-07 1.301367e-07
26 1.061224 1.061224 -1.030836 -1.030840 -1.030836 3.375118e-06 3.375118e-06
27 1.102041 1.102041 -1.051625 -1.051640 -1.051625 1.501813e-05 1.501813e-05
28 1.142857 1.142857 -1.072580 -1.072619 -1.072580 3.963123e-05 3.963123e-05
29 1.183673 1.183673 -1.093688 -1.093769 -1.093688 8.105247e-05 8.105247e-05
... ... ... ... ... ... ... ...
2470 0.816327 0.816327 -1.910603 -1.910486 -1.910603 1.169746e-04 1.169746e-04
2471 0.857143 0.857143 -1.929998 -1.929946 -1.929998 5.272612e-05 5.272612e-05
2472 0.897959 0.897959 -1.949684 -1.949665 -1.949684 1.841676e-05 1.841676e-05
2473 0.938776 0.938776 -1.969633 -1.969629 -1.969633 3.814702e-06 3.814702e-06
2474 0.979592 0.979592 -1.989822 -1.989822 -1.989822 1.355582e-07 1.355582e-07
2475 1.020408 1.020408 -2.010230 -2.010230 -2.010230 1.301367e-07 1.301367e-07
2476 1.061224 1.061224 -2.030836 -2.030840 -2.030836 3.375118e-06 3.375118e-06
2477 1.102041 1.102041 -2.051625 -2.051640 -2.051625 1.501813e-05 1.501813e-05
2478 1.142857 1.142857 -2.072580 -2.072619 -2.072580 3.963123e-05 3.963123e-05
2479 1.183673 1.183673 -2.093688 -2.093769 -2.093688 8.105247e-05 8.105247e-05
2480 1.224490 1.224490 -2.114936 -2.115079 -2.114936 1.424848e-04 1.424848e-04
2481 1.265306 1.265306 -2.136314 -2.136540 -2.136314 2.265827e-04 2.265827e-04
2482 1.306122 1.306122 -2.157811 -2.158146 -2.157811 3.355277e-04 3.355277e-04
2483 1.346939 1.346939 -2.179418 -2.179889 -2.179418 4.710941e-04 4.710941e-04
2484 1.387755 1.387755 -2.201127 -2.201762 -2.201127 6.347054e-04 6.347054e-04
2485 1.428571 1.428571 -2.222931 -2.223758 -2.222931 8.274846e-04 8.274846e-04
2486 1.469388 1.469388 -2.244822 -2.245872 -2.244822 1.050296e-03 1.050296e-03
2487 1.510204 1.510204 -2.266795 -2.268098 -2.266795 1.303782e-03 1.303782e-03
2488 1.551020 1.551020 -2.288843 -2.290432 -2.288843 1.588397e-03 1.588397e-03
2489 1.591837 1.591837 -2.310963 -2.312867 -2.310963 1.904432e-03 1.904432e-03
2490 1.632653 1.632653 -2.333148 -2.335400 -2.333148 2.252041e-03 2.252041e-03
2491 1.673469 1.673469 -2.355395 -2.358027 -2.355395 2.631259e-03 2.631259e-03
2492 1.714286 1.714286 -2.377701 -2.380743 -2.377701 3.042025e-03 3.042025e-03
2493 1.755102 1.755102 -2.400060 -2.403544 -2.400060 3.484189e-03 3.484189e-03
2494 1.795918 1.795918 -2.422470 -2.426428 -2.422470 3.957533e-03 3.957533e-03
2495 1.836735 1.836735 -2.444928 -2.449390 -2.444928 4.461780e-03 4.461780e-03
2496 1.877551 1.877551 -2.467431 -2.472427 -2.467431 4.996601e-03 4.996601e-03
2497 1.918367 1.918367 -2.489976 -2.495538 -2.489976 5.561629e-03 5.561629e-03
2498 1.959184 1.959184 -2.512561 -2.518718 -2.512561 6.156460e-03 6.156460e-03
2499 2.000000 2.000000 -2.535184 -2.541964 -2.535184 6.780667e-03 6.780667e-03

2500 rows × 7 columns

In [ ]: